Fixation dynamics on hypergraphs.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
Sep 2023
Historique:
received: 16 01 2023
accepted: 05 09 2023
revised: 06 10 2023
pubmed: 26 9 2023
medline: 26 9 2023
entrez: 26 9 2023
Statut: epublish

Résumé

Hypergraphs have been a useful tool for analyzing population dynamics such as opinion formation and the public goods game occurring in overlapping groups of individuals. In the present study, we propose and analyze evolutionary dynamics on hypergraphs, in which each node takes one of the two types of different but constant fitness values. For the corresponding dynamics on conventional networks, under the birth-death process and uniform initial conditions, most networks are known to be amplifiers of natural selection; amplifiers by definition enhance the difference in the strength of the two competing types in terms of the probability that the mutant type fixates in the population. In contrast, we provide strong computational evidence that a majority of hypergraphs are suppressors of selection under the same conditions by combining theoretical and numerical analyses. We also show that this suppressing effect is not explained by one-mode projection, which is a standard method for expressing hypergraph data as a conventional network. Our results suggest that the modeling framework for structured populations in addition to the specific network structure is an important determinant of evolutionary dynamics, paving a way to studying fixation dynamics on higher-order networks including hypergraphs.

Identifiants

pubmed: 37751462
doi: 10.1371/journal.pcbi.1011494
pii: PCOMPBIOL-D-23-00070
pmc: PMC10558078
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1011494

Informations de copyright

Copyright: © 2023 Liu, Masuda. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Ruodan Liu (R)

Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, United States of America.

Naoki Masuda (N)

Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, United States of America.
Computational and Data-Enabled Sciences and Engineering Program, State University of New York at Buffalo, Buffalo, New York, United States of America.

Classifications MeSH